Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom

Conference paper (2023)

Authors

G.M. Allen Web Information Systems - EEMCS
Katherine Landau Wright Boise State University
Jerry Alan Fails Boise State University
Casey Kennington Boise State University
M.S. Pera Web Information Systems - EEMCS
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2023 G.M. Allen, Katherine Landau Wright, Jerry Alan Fails, Casey Kennington, M.S. Pera
DOI
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https://doi.org/10.1109/WI-IAT59888.2023.00050
Ranking Children’s web search
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

Abstract

We introduce a re-ranking model that augments the functionality of standard search engines to aid classroom search activities for children (ages 6–11). This model extends the known listwise learning-to-rank framework by balancing risk and reward. Doing so enables the model to prioritize Web resources of high educational alignment, appropriateness, and adequate readability by analyzing the URLs, snippets, and page titles of Web resources retrieved by a mainstream search engine. Experimental results demonstrate the value of considering multiple perspectives inherent to the classroom when designing algorithms that can better support children's information discovery.